Recursive and iterative clustering in granular hierarchical, network, and temporal datasets

聚类是一种常用的无监督数据挖掘技术，用于对相似对象进行分组。拟议的研究计划将调查一种新的迭代方法，在粒度环境中的聚类。一个信息颗粒代表一个对象。例如，具有特定购买模式的客户可以由信息粒表示。一个颗粒通常与其他颗粒相连。例如，在分层环境中，客户颗粒将连接到多个产品颗粒，反之亦然。在粒度网络中，电话用户连接到其他电话用户。在粒度时间环境中，事件的日常模式与历史和未来的日常模式相关联。传统上，颗粒的聚类是孤立地进行的，没有任何关于连接颗粒聚类的信息。所提出的研究的主要主题是同时集群的所有颗粒迭代。每次迭代将使用先前连接颗粒的聚类结果，直到实现所有颗粒的稳定聚类。在客户和产品这样的分层环境中，这意味着客户的聚类使用产品聚类的配置文件，反之亦然。对于联网的颗粒，使用其他连接的用户的集群配置文件对电话用户进行集群。在时间粒度聚类中，日常模式将基于历史和未来模式的聚类简档来聚类。聚类的这些重复应用在层次结构中被称为迭代，在网络中被称为递归。层次、网络和时态数据的集成元聚类是一个多方面的工程。由于聚类是无监督的，我们不知道预期的结果，因此研究结果聚类的质量很重要。除了获得定量评估之外，偏好的概念将用于基于集群与更理想对象的良好连接来评估集群。迭代和递归算法将进一步修改为模糊和粗糙聚类，这允许一个对象属于多个聚类。我们计划为零售、移动的电话、工程和金融数据集设计、开发、实现和测试聚类算法的变体。

Clustering is one of the frequently used unsupervised data mining techniques for grouping similar objects. The proposed research program will investigate a novel iterative approach to clustering in a granular environment. An information granule represents an object. For example, a customer with certain purchasing patterns could be represented by an information granule. A granule is usually connected to other granules. For example, in a hierarchical environment, a customer granule will be connected to a number of product granules and vice versa. In a granular network, phone users are connected to other phone users. In a granular temporal environment, a daily pattern of events is connected to historical and future daily patterns. Traditionally, clustering of granules is done in isolation without any information on clustering of the connected granules. The primary theme of the proposed research is to simultaneously cluster all the granules iteratively. Each iteration will use results of previous clustering of connected granules, until a stable clustering of all the granules is achieved. In a hierarchical environment such as customers and products, it will mean that clustering of customers uses profiles of product clusters, and vice versa. For networked granules, a phone user is clustered using cluster profiles of the other connected users. In a temporal granular clustering, daily patterns will be clustered based on clustered profiles of historical and future patterns. These repeated applications of clustering are termed iterative in a hierarchy and are termed recursive in networks. The integrated meta-clustering of hierarchical, network, and temporal data is a multi-faceted project. Since clustering is unsupervised and we do not know the expected outcomes, it is important to study the quality of the resultant clustering. In addition to deriving quantitative evaluations, the notion of preference will be used to value a cluster based on how well-connected it is to more desirable objects. The iterative and recursive algorithms will be further modified for fuzzy and rough clustering, which allow an object to belong to multiple clusters. We plan to design, develop, implement, and test variations of the clustering algorithms for retail, mobile phone, engineering, and financial datasets.